I also blogged about a part of this talk here (why a mad scientist’s attempt at creating A.I. to make new scientific discoveries was doomed).

The talk was given a prise for best talk by the judging panel which included David Krakauer, Tom Carter and best-selling author Cormac McCarthy. At several points in the talk, I completely forget what I was supposed to say because the people filming the event asked me to set my screen up in a way so I couldn’t see my notes.

Guttal V, & Couzin ID (2010). Social interactions, information use, and the evolution of collective migration. Proceedings of the National Academy of Sciences of the United States of America, 107 (37), 16172-7 PMID: 20713700

Lee & Hasegawa (2011) use phylogenetic methods to trace the origins of Japonic languages and dialects. Two hypotheses are considered: First, the farming/language dispersal hypothesis posits that the main factor for the divergence of genetic and linguistic diversity was agricultural expansion. Second, the diffusion/transformation hypothesis posits that cultural innovations such as farming can diffuse between societies, and so genetic and linguistic diversity should not be linked. The estimate of the common linguistic ancestor was in accordance with the farming/language dispersal hypothesis, again suggesting that that linguistic diversity followed genetic diversity.

The study is notable in considering dialects as well as languages and using etymology dictionaries to reconstruct forms from Middle and Old Japanese. The analysis is also done with their own reconstructions and another, unrelated set. The technique is similar to that used by Russel Gray et al. (2009) to study Pacific settlement patterns.

Simon Greenhill has just announced two new papers on applying phylogenetic techniques to the study of culture. No doubt I’ll be blogging about these at some point in the future. Below are the abstracts:

The Boston Globe reported today that Marc Hauser is on leave due to scientific misconduct . The Great Beyond summarises the article as follows:

The trouble centers on a 2002 paper published in the journal Cognition (subscription required). Hauser was the first author on the paper, which found that cotton-top tamarins are able to learn patterns – previously thought to be an important step in language acquisition. The paper has been retracted, for reasons which are reportedly unclear even to the journal’s editor, Gerry Altmann.

Two other papers, a 2007 article in Proceedings of the Royal Society B and a 2007 Science paper, were also flagged for investigation. A correction has been published on the first, and Science is now looking into concerns about the second. And the Globe article highlights other controversies, including a 2001 paper in the American Journal of Primatology, which has not been retracted although Hauser himself later said he was unable to replicate the results. Findings in a 1995 PNAS paper were also questioned by an outside researcher, Gordon Gallup of the State University of New York at Albany, who reviewed the original data and said he found “not a thread of compelling evidence” to support the paper’s conclusions.

What makes humans unique? This never-ending debate has sparked a long list of proposals and counter-arguments and, to quote from a recent article on this topic,

“a similar fate most likely awaits some of the claims presented here. However such demarcations simply have to be drawn once and again. They focus our attention, make us wonder, and direct and stimulate research, exactly because they provoke and challenge other researchers to take up the glove and prove us wrong.” (Høgh-Olesen 2010: 60)

In this post, I’ll focus on six candidates that might play a part in constituting what makes human cognition unique, though there are countless others (see, for example, here).

One of the key candidates for what makes human cognition unique is of course language and symbolicthought. We are “the articulate mammal” (Aitchison 1998) and an “animal symbolicum” (Cassirer 2006: 31). And if one defining feature truly fits our nature, it is that we are the “symbolic species” (Deacon 1998). But as evolutionary anthropologists Michael Tomasello and his colleagues argue,

“saying that only humans have language is like saying that only humans build skyscrapers, when the fact is that only humans (among primates) build freestanding shelters at all” (Tomasello et al. 2005: 690).

Language and Social Cognition

According to Tomasello and many other researchers, language and symbolic behaviour, although they certainly are crucial features of human cognition, are derived from human beings’ unique capacities in the social domain. As Willard van Orman Quine pointed out, language is essential a “social art” (Quine 1960: ix). Specifically, it builds on the foundations of infants’ capacities for joint attention, intention-reading, and cultural learning (Tomasello 2003: 58). Linguistic communication, on this view, is essentially a form of joint action rooted in common ground between speaker and hearer (Clark 1996: 3 & 12), in which they make “mutually manifest” relevant changes in their cognitive environment (Sperber & Wilson 1995). This is the precondition for the establishment and (co-)construction of symbolic spaces of meaning and shared perspectives (Graumann 2002, Verhagen 2007: 53f.). These abilities, then, had to evolve prior to language, however great language’s effect on cognition may be in general (Carruthers 2002), and if we look for the origins and defining features of human uniqueness we should probably look in the social domain first.

Corroborating evidence for this view comes from comparisons of brain size among primates. Firstly, there are significant positive correlations between group size and primate neocortex size (Dunbar & Shultz 2007). Secondly, there is also a positive correlation between technological innovation and tool use – which are both facilitated by social learning – on the one hand and brain size on the other (Reader and Laland 2002). Our brain, it seems, is essential a “social brain” that evolved to cope with the affordances of a primate social world that frequently got more complex (Dunbar & Shultz 2007, Lewin 2005: 220f.).

Thus, “although innovation, tool use, and technological invention may have played a crucial role in the evolution of ape and human brains, these skills were probably built upon mental computations that had their origins and foundations in social interactions” (Cheney & Seyfarth 2007: 283).

For me, recent computational accounts of language evolution provide a compelling rationale that cultural, as opposed to biological, evolution is fundamental in understanding the design features of language. The basis for this rests on the simple notion of language being not only a conveyor of cultural information, but also a socially learned and culturally transmitted system: that is, an individual’s linguistic knowledge is the result of observing the linguistic behaviour of others. Here, this well-attested process of language acquisition, often termed Iterated Learning, emphasises the effects of differential learnability on competing linguistic variants. Sounds, words and grammatical structures are therefore seen to be the products of selection and directed mutation. As you can see from the use of terms such as selection and mutation it’s clear we can draw many parallels between the literature on language evolution and analogous processes in biology. Indeed, Darwin himself noted such similarities in the Descent of Man. However, one aspect evolutionary linguists don’t seem to borrow is that of a null model. Is it possible that the changes we see in languages over time are just the products of processes analogous to genetic drift?

Here is a far-reaching and crucially relevant question for those of us seeking to understand the evolution of culture: Is there any relationship between population size and tool kit diversity or complexity? This question is important because, if met with an affirmative answer, then the emergence of modern human culture may be explained by changes in population size, rather than a species-wide cognitive explosion. Some attempts at an answer have led to models which make certain predictions about what we expect to see when populations vary. For instance, Shennan (2001) argues that in smaller populations, the number of people adopting a particular cultural variant is more likely to be affected by sampling variation. So in larger populations, learners potentially have access to a greater number of experts, which means adaptive variants are less likely to be lost by chance (Henrich, 2004).

Models aside, and existing empirical evidence is limited with the results being mixed. I previously mentioned the gradual loss of complexity in Tasmanian tool kits after the population was isolated from mainland Australia. Elsewhere, Golden (2006) highlighted the case of isolated Polar Inuit, who lost kayaks, the bow and arrow and other technologies when their knowledgeable experts were wiped out during a plague.Yet two systematic studies (Collard et al., 2005; Read, 2008) of the Inuit case found no evidence for population size being a predictor of technological complexity.